from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

Daal4py_kmeans_short: 0h 0m 1s

Daal4py_ridge: 0h 0m 2s

Kmeans_short: 0h 0m 3s

Daal4py_logisticregression: 0h 0m 4s

Daal4py_kmeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

Logisticregression: 0h 0m 20s

Kmeans_tall: 0h 0m 24s

Daal4py_kneighborsclassifier_kd_tree: 0h 0m 31s

Kneighborsclassifier_kd_tree: 0h 2m 44s

Daal4py_kneighborsclassifier: 0h 2m 58s

Lightgbm: 0h 5m 0s

Catboost_symmetric: 0h 5m 10s

Histgradientboostingclassifier: 0h 5m 15s

Xgboost: 0h 5m 24s

Catboost: 0h 5m 43s

Kneighborsclassifier: 0h 35m 13s

Total: 1h 9m 19s

print_env_info()

Benchmark environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config_file_path="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.144	0.000	5.544	0.000	1	5	NaN	NaN	0.516	0.000	0.280	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.194	0.085	0.000	0.024	1	5	0.830	0.942	2.132	0.029	11.349	0.160	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.203	0.002	0.000	0.203	1	5	1.000	1.000	0.086	0.000	2.369	0.021	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.133	0.000	6.024	0.000	1	1	NaN	NaN	0.486	0.000	0.273	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.275	0.057	0.000	0.013	1	1	0.711	0.811	2.136	0.063	6.215	0.185	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.187	0.002	0.000	0.187	1	1	1.000	1.000	0.088	0.001	2.133	0.032	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.136	0.000	5.884	0.000	1	100	NaN	NaN	0.486	0.000	0.280	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.424	0.441	0.000	0.024	1	100	0.929	0.688	2.159	0.098	11.313	0.554	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.208	0.003	0.000	0.208	1	100	1.000	1.000	0.093	0.005	2.246	0.122	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.141	0.000	5.688	0.000	-1	1	NaN	NaN	0.497	0.000	0.283	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.357	0.173	0.000	0.025	-1	1	0.711	0.942	2.142	0.070	11.838	0.393	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.188	0.021	0.000	0.188	-1	1	1.000	1.000	0.088	0.003	2.135	0.255	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.128	0.000	6.236	0.000	-1	5	NaN	NaN	0.479	0.000	0.268	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	36.209	0.000	0.000	0.036	-1	5	0.830	0.688	2.031	0.021	17.829	0.184	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.187	0.017	0.000	0.187	-1	5	1.000	1.000	0.091	0.003	2.065	0.203	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.000	6.218	0.000	-1	100	NaN	NaN	0.490	0.000	0.263	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.681	0.000	0.000	0.036	-1	100	0.929	0.811	2.104	0.096	16.959	0.775	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.177	0.016	0.000	0.177	-1	100	1.000	1.000	0.093	0.012	1.898	0.299	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.268	0.000	1	5	NaN	NaN	0.113	0.000	0.528	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.525	0.137	0.000	0.022	1	5	0.986	0.989	0.412	0.002	52.230	0.427	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.024	0.001	0.000	0.024	1	5	1.000	1.000	0.007	0.000	3.675	0.133	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.265	0.000	1	1	NaN	NaN	0.113	0.000	0.535	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.643	0.033	0.000	0.011	1	1	0.976	0.991	0.350	0.003	30.369	0.246	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.007	0.000	2.199	0.099	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.061	0.000	0.263	0.000	1	100	NaN	NaN	0.113	0.000	0.536	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.977	0.139	0.000	0.022	1	100	0.985	0.983	0.354	0.007	62.001	1.281	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.025	0.001	0.000	0.025	1	100	1.000	1.000	0.009	0.003	2.841	0.976	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.062	0.000	0.260	0.000	-1	1	NaN	NaN	0.115	0.000	0.538	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	22.017	0.097	0.000	0.022	-1	1	0.976	0.989	0.427	0.010	51.557	1.237	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.002	0.000	0.020	-1	1	1.000	1.000	0.007	0.000	2.914	0.340	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.268	0.000	-1	5	NaN	NaN	0.114	0.000	0.521	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.887	0.000	0.000	0.033	-1	5	0.986	0.983	0.358	0.019	91.795	4.755	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.002	0.000	0.030	-1	5	1.000	1.000	0.007	0.000	4.268	0.314	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.268	0.000	-1	100	NaN	NaN	0.112	0.000	0.530	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.154	0.000	0.000	0.033	-1	100	0.985	0.991	0.359	0.018	92.352	4.627	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.031	0.002	0.000	0.031	-1	100	1.000	1.000	0.006	0.000	4.821	0.372	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.227	0.000	0.025	0.000	-1	1	NaN	NaN	0.807	0.000	3.999	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.456	0.022	0.000	0.000	-1	1	0.960	0.975	0.615	0.018	0.741	0.041	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.001	0.000	4.141	1.393	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.268	0.000	0.024	0.000	1	5	NaN	NaN	0.797	0.000	4.101	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.447	0.019	0.000	0.001	1	5	0.969	0.974	0.196	0.004	7.387	0.167	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.000	0.000	3.825	1.630	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.218	0.000	0.025	0.000	-1	5	NaN	NaN	0.763	0.000	4.217	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.851	0.033	0.000	0.001	-1	5	0.969	0.962	0.118	0.005	7.230	0.400	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	5	1.000	1.000	0.000	0.000	16.469	7.112	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.256	0.000	0.025	0.000	1	100	NaN	NaN	0.771	0.000	4.225	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.837	0.046	0.000	0.005	1	100	0.971	0.975	0.616	0.017	7.852	0.235	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	1	100	1.000	1.000	0.001	0.000	4.164	1.499	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.237	0.000	0.025	0.000	1	1	NaN	NaN	0.769	0.000	4.209	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.761	0.005	0.000	0.001	1	1	0.960	0.974	0.208	0.011	3.659	0.199	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	3.026	1.307	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.254	0.000	0.025	0.000	-1	100	NaN	NaN	0.787	0.000	4.133	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.751	0.028	0.000	0.003	-1	100	0.971	0.962	0.119	0.013	23.210	2.631	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.007	0.001	0.000	0.007	-1	100	1.000	1.000	0.000	0.000	25.467	9.997	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.883	0.000	0.018	0.000	-1	1	NaN	NaN	0.530	0.000	1.666	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.000	0.001	0.000	-1	1	0.970	0.990	0.007	0.002	3.674	0.784	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	17.719	12.273	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.873	0.000	0.018	0.000	1	5	NaN	NaN	0.529	0.000	1.651	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.001	0.001	0.000	1	5	0.978	0.987	0.001	0.000	19.177	5.914	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	6.025	4.499	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.877	0.000	0.018	0.000	-1	5	NaN	NaN	0.529	0.000	1.658	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.029	0.001	0.001	0.000	-1	5	0.978	0.980	0.001	0.000	34.501	8.956	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	26.936	19.593	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.931	0.000	0.017	0.000	1	100	NaN	NaN	0.550	0.000	1.691	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.056	0.001	0.000	0.000	1	100	0.981	0.990	0.008	0.000	7.167	0.408	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.547	3.749	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.860	0.000	0.019	0.000	1	1	NaN	NaN	0.525	0.000	1.640	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	1	1	0.970	0.987	0.001	0.000	22.102	4.461	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.941	4.172	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.870	0.000	0.018	0.000	-1	100	NaN	NaN	0.535	0.000	1.626	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.050	0.003	0.000	0.000	-1	100	0.981	0.980	0.001	0.000	49.773	15.297	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	21.696	16.360	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.711	0.000	0.675	0.000	k-means++	NaN	30	NaN	0.481	0.0	1.478	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.281	0.000	k-means++	0.001	30	0.000	0.000	0.0	10.154	5.668	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	12.514	8.762	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.570	0.000	0.842	0.000	random	NaN	30	NaN	0.508	0.0	1.123	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.323	0.000	random	0.001	30	0.001	0.000	0.0	9.526	5.511	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	11.148	7.574	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.920	0.000	3.468	0.000	k-means++	NaN	30	NaN	2.908	0.0	2.379	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	12.180	0.000	k-means++	0.002	30	0.002	0.000	0.0	6.812	3.309	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.016	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.973	6.381	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.286	0.000	3.818	0.000	random	NaN	30	NaN	3.122	0.0	2.013	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	13.265	0.000	random	0.002	30	0.002	0.000	0.0	6.212	2.628	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.017	0.001	random	1.000	30	1.000	0.000	0.0	9.857	5.616	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.272	0.000	0.012	0.000	k-means++	NaN	20	NaN	0.037	0.0	7.454	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.165	0.000	k-means++	0.000	20	0.004	0.001	0.0	2.658	0.377	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.704	5.572	See	See
3	KMeans_short	fit	10000	10000	2	20	0.087	0.000	0.037	0.000	random	NaN	20	NaN	0.096	0.0	0.910	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.160	0.000	random	0.003	20	0.004	0.001	0.0	2.761	0.446	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	random	1.000	20	1.000	0.000	0.0	10.707	6.447	See	See
6	KMeans_short	fit	10000	10000	100	20	0.716	0.000	0.223	0.000	k-means++	NaN	20	NaN	0.154	0.0	4.643	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.004	0.002	4.110	0.000	k-means++	0.309	20	0.261	0.001	0.0	3.322	1.484	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.011	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.478	3.681	See	See
9	KMeans_short	fit	10000	10000	100	20	0.229	0.000	0.700	0.000	random	NaN	20	NaN	0.383	0.0	0.597	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.000	5.674	0.000	random	0.295	20	0.343	0.001	0.0	2.202	0.439	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.000	0.010	0.002	random	1.000	20	1.000	0.000	0.0	8.711	4.608	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.617	0.0	[-0.10156824]	0.000	NaN	NaN	NaN	NaN	NaN	1.950	0.0	5.958	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[45.47105455]	0.000	NaN	NaN	NaN	NaN	0.517	0.000	0.0	0.887	0.334	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.21762667]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.363	0.349	See	See
3	LogisticRegression	fit	1000	1000	10000	[28]	0.829	0.0	[-2.47938816]	0.001	NaN	NaN	NaN	NaN	NaN	0.724	0.0	1.145	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[28]	0.002	0.0	[120.02344744]	0.000	NaN	NaN	NaN	NaN	0.220	0.003	0.0	0.594	0.112	See	See
5	LogisticRegression	predict	1000	1	10000	[28]	0.000	0.0	[19.69909629]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.142	0.084	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.208	0.0	0.385	0.0	NaN	NaN	NaN	0.200	0.000	1.039	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	7.846	0.0	NaN	NaN	0.093	0.017	0.001	0.608	0.028	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.085	0.0	NaN	NaN	NaN	0.000	0.000	0.651	0.549	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.447	0.0	0.553	0.0	NaN	NaN	NaN	0.262	0.000	5.531	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	1.807	0.0	NaN	NaN	1.000	0.000	0.000	1.751	1.965	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.012	0.0	NaN	NaN	NaN	0.000	0.000	0.638	0.625	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier_symmetric.csv",
])
reporting_hpo.run()

Raw results

Randomized HPO Curves

25th percentile

Mean

75th percentile